A Computational Analysis on Gene Fusions in Human Cancer

Abstract

Gene fusions are instances where two discrete genes incorrectly join together. They are common mutations in cancer, and, since the advent of next generation sequencing technology, many gene fusions in cancer tissues have been discovered and cataloged. We utilized the rapidly growing pool of information on gene fusions in human cancer to form projections on gene fusion mutations. We test two hypotheses: 1) identifiable motifs and entropy patterns exist at breakpoints that form fusions, and 2) gene fusions are more connected than randomly generated mutations in the biological networks. This thesis project has three related computational analyses: 1) motif discovery to examine common sequence patterns at and around breakpoints that form fusions, 2) entropy sliding-window analysis to determine structural characteristics at and around breakpoints that form fusions, and 3) gene-fusion network analysis to visualize and compare cancer-associated gene fusion metrics versus controls. We found no over-represented motifs at breakpoints that form gene fusions. We characterized a common entropy change at breakpoints. This feature may help us to predict gene fusions as part of prediction algorithms. Finally, we found that network metrics may be useful toward understanding the role gene fusions have in cancers.